Biology subjects

Shah, A. D.

Publications and source records attributed to Shah, A. D..

4 recordsLinked to original sources

bioRxiv2022

Systems-level investigation of mucopolysaccharidosis IIIA identifies deficient synaptic activity as a key driver of disease progression

Mucopolysaccharidoses are lysosomal storage diseases that collectively represent a major cause of lethal, treatment-refractory childhood dementias 1-7 Clinically-useful interventions are hampered due to an incomplete understanding of their neuropathological origins. Using the zebrafish sgsh model of mucopolysaccharidosis IIIA 8 (MPS IIIA, Sanfilippo syndrome A), we conducted several omics-based analyses, and developed and benchmarked a novel bioinformatic feature classification and ranking model for high-throughput datasets - ExIR - to prioritise important features in the progression of neurological manifestations of the disease. We find that the massive endolysosomal burden resulting from increased lysosomal storage of heparan sulfate and other secondarily accumulating substrates, such as sphingolipids, induces abnormal microtubule organisation and vesicle trafficking in neurons. This results in a gradual impairment of synaptic vesicle localisation at the presynaptic terminal and consequently impaired neuronal activity. Importantly, the endolysosomal phenotype in MPS IIIA zebrafish well-precedes the onset of neural pathology, though the larval MPS IIIA brain was found to be more susceptible to perturbation than wild type siblings. Collectively, these analyses demonstrate the presence of a progressive functional neurodegenerative phenotype underpinning neurological disease in MPS IIIA. Our findings provide direct mechanistic evidence linking the well-described lysosomal storage basis for MPS IIIA to its disproportionately severe neural clinical involvement, enabling development and refinement of future therapeutic interventions for this currently untreatable disorder. HighlightsO_LIMPS IIIA represents one of the most common causes of broadly fatal childhood dementia, but the mechanisms underlying disease progression are poorly understood. C_LIO_LIThe first systems-level analyses of disease state and progression in the CNS of an MPS IIIA animal model were performed. C_LIO_LIExperimental data-based Integrative Ranking (ExIR) was developed to provide unbiased prioritisation and classification of biological data as drivers, biomarkers and mediators of biological processes from high-throughput data at a systems level. C_LIO_LIApplication of ExIR to a transcriptomic and proteomic analyses of a zebrafish model of MPS IIIA implies progressive deficiencies in synaptic activity as a key driver of disease progression correlating with progressive neuronal endolysosomal burden and secondary storage diseases. C_LIO_LIA novel unifying explanation of pathobiology and progression of MPS IIIA facilitates identification of clinically targetable features and may be generalised to other neuronopathic storage disorders. C_LI

neuroscience↗

bioRxiv2021

Perception of soft materials relies on physics-based object representations: Behavioral and computational evidence

When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones - including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior.

neuroscience↗

bioRxiv2019

Bleeding in cardiac patients prescribed antithrombotic drugs: Electronic health record phenotyping algorithms, incidence, trends and prognosis

BackgroundClinical guidelines and public health authorities lack recommendations on scalable approaches to defining and monitoring the occurrence and severity of bleeding in populations prescribed antithrombotic therapy. We aimed to develop electronic health record algorithms for different bleeding phenotypes, and to determine the incidence, time trends and prognosis of bleeding in patients with incident cardiac disorders indicated for antiplatelet and/or vitamin K antagonist (VKA) therapy. MethodsWe examined linked primary care, hospital admission and death registry electronic health records (CALIBER 1998-2010, England) of patients with newly diagnosed atrial fibrillation, acute myocardial infarction, unstable angina or stable angina to develop algorithms for bleeding events. Kaplan-Meier plots were used to estimate the incidence of bleeding events and we used Cox regression models to assess prognosis for all-cause mortality, atherothrombotic events and further bleeding following bleeding events. ResultsWe present electronic health record phenotyping algorithms for bleeding based on bleeding diagnosis in primary or hospital care, symptoms, transfusion, surgical procedures, and haemoglobin values. In validation of the phenotype we estimated a positive predictive value of 0.88 (95% Cl: 0.64, 0.99) for hospitalised bleeding. Amongst 128,815 patients, 27259 (21.2%) had at least one bleeding event, with 5 year risks of bleeding of 29.1%, 21.9%, 25.3% and 23.4% following diagnoses of atrial fibrillation, acute myocardial infarction, unstable angina and stable angina respectively. Rates of hospitalised bleeding per 1000 patients more than doubled from 1.02 (95% Cl: 0.83, 1.22) in January 1998 to 2.68 (95% Cl: 2.49, 2.88) in December 2009 coinciding with increased rates of antiplatelet and VKA prescribing. Patients with hospitalised bleeding and primary care bleeding, with or without markers of severity, were at increased risk of all-cause mortality and atherothrombotic events compared to those with no bleeding. For example the hazard ratio for all-cause mortality was 1.98 (95% Cl: 1.86, 2.11) for primary care bleeding with markers of severity, and 1.99 (95% Cl: 1.92, 2.05) for hospitalised bleeding without markers of severity, compared to patients with no bleeding. ConclusionsElectronic health record bleeding phenotyping algorithms offer a scalable approach to monitoring bleeding in the population. Incidence of bleeding has doubled in incidence since 1998, affects 1 in 4 cardiac patients, and is associated with poor prognosis. Efforts are required to tackle this iatrogenic epidemic. O_LSTWhat is already known?C_LSTO_LIClinical guidelines and public health authorities lack recommendations on how to define or monitor the occurrence and severity of bleeding in populations. C_LIO_LIThis is particularly important because clinical guidelines increasingly recommend the use of one, two or three antiplatelet and vitamin K antagonist drugs to lower the risk of subsequent atherothrombotic events in common heart diseases including atrial fibrillation, acute coronary syndromes and chronic stable angina. C_LIO_LIClinical guidelines lack consistent recommendations of how to reduce the main side effect of bleeding. C_LIO_LIFor acute myocardial infarction it has been shown that combining primary care electronic health records (which include information from hospital discharge summaries) and hospital admission data can generate valid EHR disease phenotypes and provide real-world estimates of disease occurrence. C_LI O_LSTWhat is not known?C_LSTO_LIIt is not known how to define bleeding occurrence and severity in large scale, unselected populations by combining available information on bleeding diagnosis in primary or hospital care, symptoms, transfusion, surgical procedures, and haemoglobin values. C_LIO_LIThe population-based incidence, time trends and long-term prognosis of bleeding have not been evaluated in people with common cardiac disorders. C_LIO_LIComparisons of the population burden of bleeding across common cardiac disorders, such as atrial fibrillation, acute coronary syndromes and stable angina, are lacking. C_LI O_LSTWhat this study adds?C_LSTO_LIPhenotype: We developed standardised replicable EHR phenotyping algorithms defining bleeding and severity measures based on available clinical information across structured primary and hospital care EHR sources. C_LIO_LIIncidence: At 5 years of follow-up, one in five patients with cardiac disease had a bleeding event and 6.5% had fatal or severe bleeding. C_LIO_LITrends: There was approximately a two-fold increase in incidence of primary care and hospitalised bleeding between 1998 and 2010. The rate of fatal bleeding remained stable. C_LIO_LIPrognosis: Patients with bleeding recorded in primary care or in hospital admissions are at increased bleeding between 1998 and 2010. The rate of fatal bleeding remained stable, risk of all-cause death and atherothrombotic events. C_LI

epidemiology↗

bioRxiv2018

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values in datasets are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning.\n\nUsing a cohort of over 80,000 patients from the CALIBER programme, we performed a systematic comparison of several machine-learning approaches in EHR. We used Cox models and random survival forests with and without imputation on 27 expert-selected variables to predict all-cause mortality. We also used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input.\n\nWe observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values, and with no need to scale or transform continuous data. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values.\n\nWe also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate.\n\nThis demonstrates that machine-learning approaches applied to raw EHR data can be used to build reliable models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research.

epidemiology↗